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# SPDX-License-Identifier: Apache-2.0
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
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from typing import Optional

import torch
import wandb

from cosmos_predict2._src.imaginaire.callbacks.every_n import EveryN
from cosmos_predict2._src.imaginaire.model import ImaginaireModel
from cosmos_predict2._src.imaginaire.trainer import ImaginaireTrainer
from cosmos_predict2._src.imaginaire.utils import distributed, log


class LogWeight(EveryN):
    def __init__(
        self,
        every_n: Optional[int] = 100,
        step_size: int = 1,
        barrier_after_run: bool = True,
        run_at_start: bool = False,
    ):
        super().__init__(
            every_n=every_n,
            step_size=step_size,
            barrier_after_run=barrier_after_run,
            run_at_start=run_at_start,
        )

    def every_n_impl(
        self,
        trainer: ImaginaireTrainer,
        model: ImaginaireModel,
        data_batch: dict[str, torch.Tensor],
        output_batch: dict[str, torch.Tensor],
        loss: torch.Tensor,
        iteration: int,
    ) -> None:
        if "logging_dict" in output_batch:
            logging_dict = output_batch["logging_dict"]

            if distributed.is_rank0():
                info = {}
                for k, v in logging_dict.items():
                    info[f"model_weight/{k}"] = v

                if info and wandb.run:
                    wandb.log(info, step=iteration)

                log.info(f"Log weight at iteration {iteration}: {info}")